ArticlePloS one2019
A novel model for malaria prediction based on ensemble algorithms.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.
- The role of artificial intelligence (AI) in the elimination of malaria: a systematic literature review.Malaria journal · 2025Pooled it
- Ensemble Machine Learning for Malaria Diagnosis in Resource-Limited Settings Using Clinical and Demographic Features.Infectious disease reports · 2026Article
- A Machine Learning-Based Dynamic SST Index for Long-Lead Malaria Prediction in the Peruvian Amazon.GeoHealth · 2026Article
- Integrating machine learning and spatial clustering for malaria case prediction in Brazil's Legal Amazon.BMC infectious diseases · 2025Article
- Hybrid models combining trend and seasonality components with machine learning algorithms provide accurate forecasting of malaria incidence.PLOS global public health · 2025Article
- Advancing Early Warning Systems for Malaria: Progress, challenges, and future directions - A scoping review.PLOS global public health · 2025Article
- Artificial intelligence in epidemic watch: revolutionizing infectious diseases surveillance.Frontiers in digital health · 2025Review
- Fine-Scale Spatial Prediction on the Risk ofJournal of Korean medical science · 2024Article
- Dynamics of Malaria Incidence in Khyber Pakhtunkhwa, Pakistan: Unveiling Rapid Growth Patterns and Forecasting Future Trends.Journal of epidemiology and global health · 2024Article
- Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.BMC infectious diseases · 2024Article
- Time series analysis of malaria cases to assess the impact of various interventions over the last three decades and forecasting malaria in India towards the 2030 elimination goals.Malaria journal · 2024Article
- Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model.Scientific reports · 2023Article
- Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients.PLoS neglected tropical diseases · 2023Article
- Machine learning based efficient prediction of positive cases of waterborne diseases.BMC medical informatics and decision making · 2023Article
- Specialist hybrid models with asymmetric training for malaria prevalence prediction.Frontiers in public health · 2023Article
- Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: Evidence from rural Madagascar.PLOS global public health · 2023Article
- Article
- Development and assessment of novel machine learning models to predict medication non-adherence risks in type 2 diabetics.Frontiers in public health · 2022Article
- Predicting the Number of Reported Pulmonary Tuberculosis in Guiyang, China, Based on Time Series Analysis Techniques.Computational and mathematical methods in medicine · 2022Article
- Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning.Frontiers in public health · 2022Article
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveMost previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can solve this problem by combining distinct algorithms and models. This study compares the performance of traditional time series models and deep learning algorithms in malaria case prediction and explores the application value of stacking methods in the field of infectious disease prediction.
methodsThe ARIMA, STL+ARIMA, BP-ANN and LSTM network models were separately applied in simulations using malaria data and meteorological data in Yunnan Province from 2011 to 2017. We compared the predictive performance of each model through evaluation measures: RMSE, MASE, MAD. In addition, gradient-boosting regression trees (GBRTs) were used to combine the above four models. We also determined whether stacking structure improved the model prediction performance.
resultsThe root mean square errors (RMSEs) of the four sub-models were 13.176, 14.543, 9.571 and 7.208; the mean absolute scaled errors (MASEs) were 0.469, 0.472, 0.296 and 0.266 and the mean absolute deviation (MAD) were 6.403, 7.658, 5.871 and 5.691. After using the stacking architecture combined with the above four models, the RMSE, MASE and MAD values of the ensemble model decreased to 6.810, 0.224 and 4.625, respectively.
conclusionsA novel ensemble model based on the robustness of structured prediction and model combination through stacking was developed. The findings suggest that the predictive performance of the final model is superior to that of the other four sub-models, indicating that stacking architecture may have significant implications in infectious disease prediction.
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